CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

作者全名："Zhang, C.; Yang, G.; Li, F.; Wen, Y.; Yao, Y.; Shu, H.; Simon, A.; Dillenseger, J. -l.; Coatrieux, J. -l."

作者地址："[Zhang, C.; Yang, G.; Yao, Y.; Shu, H.] Southeast Univ, Key Lab Comp Network & Informat Integrat, LIST, Minist Educ, Nanjing 210096, Peoples R China; [Zhang, C.; Yang, G.; Yao, Y.; Shu, H.] Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China; [Zhang, C.; Yang, G.; Yao, Y.; Shu, H.; Simon, A.; Dillenseger, J. -l.; Coatrieux, J. -l.] Ctr Rech Informat Biomedicale Sino Francais CRIBs, F-35000 Rennes, France; [Li, F.; Shu, H.] Chongqing Med Univ, State Key Lab Ultrasound Med & Engn, Chongqing 400016, Peoples R China; [Wen, Y.] Natl Engn Res Ctr Ultrasound Med, Chongqing 401121, Peoples R China; [Simon, A.; Dillenseger, J. -l.; Coatrieux, J. -l.] Univ Rennes, Inserm, LTSI, UMR1099, F-35000 Rennes, France"

通信作者："Shu, H (通讯作者)，Southeast Univ, Jiangsu Prov Joint Int Res Lab Med Informat Proc, Nanjing 210096, Peoples R China."

来源：IRBM

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000963644500001

JCR分区：Q1

影响因子：5.6

年份：2023

卷号：44

期号：3

开始页：　

结束页：　

文献类型：Article

关键词：HIFU therapy; Semi-supervised segmentation; Threshold-adaptation; Uterine fibroids

摘要："Objectives: The accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high -intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data. Materials and Methods: To address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet. Results: We compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data). Conclusion: Experimental results are provided to illustrate the effectiveness of the proposed semi -supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment. (c) 2022 AGBM. Published by Elsevier Masson SAS. All rights reserved."

基金机构："National Key Research and Development Program of China [2021ZD0113202]; National Natural Science Foundation [62171125, 31800825, 31640028]; Natural Science Foundation of Jiangsu Province [BE2019748]; China Scholarship Council [201906090391]"

基金资助正文："This work was supported by the National Key Research and Development Program of China (2021ZD0113202) and National Natural Science Foundation under grants (62171125, 31800825, 31640028) and Natural Science Foundation of Jiangsu Province under grant (BE2019748) , and in part by the China Scholarship Council under NO. 201906090391."